Potential Multidisciplinary Use of Large Language Models for Addressing Queries in Cardio‐Oncology

Abstract

n the crossroads of digital health and education, large language models (LLMs) emerge as tools with great potential. Trained on expansive textual data sets, these state-of-the-art artificial intelligence models can generate multidisciplinary content, answer intricate queries, and accelerate information delivery. articularly in the field of cardio-oncology, which combines cardiac and oncological expertise, LLMs have the potential to provide valuable insights to specialists like cardiologists and oncologists. 2 This is useful in situations in which standard guidelines are not immediately available or when there is a need to combine a vast amount of interdisciplinary information. However, the performances of LLMs in this context remains largely unknown. This study aims to benchmark these state-of-the-art artificial intelligence models in their ability to handle the interdisciplinary queries inherent in cardio-oncology, where integrative insights from cardiology and oncology are crucial.

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